Microbiome data integration workflow for population cohort
studies
Package demo
# Get starting time
starting_time <- Sys.time()
################################################################################
# List of packages that we need
packages <- c("ggplot2", "mia", "miaViz")
# Get packages that are already installed installed
packages_already_installed <- packages[ packages %in% installed.packages() ]
# Get packages that need to be installed
packages_need_to_install <- setdiff( packages, packages_already_installed )
# Loads BiocManager into the session. Install it if it not already installed.
if( !require("BiocManager") ){
install.packages("BiocManager")
library("BiocManager")
}
# If there are packages that need to be installed, installs them with BiocManager
# Updates old packages.
if( length(packages_need_to_install) > 0 ) {
install(packages_need_to_install, ask = FALSE)
}
# Load all packages into session. Stop if there are packages that were not
# successfully loaded
if( any(!sapply(packages, require, character.only = TRUE)) ){
stop("Error in loading packages into the session.")
}
################################################################################
# Additional setup
# Set black and white theme for figures, and Arial font
theme <- theme_bw() + theme(text = element_text(family = "Arial"),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
theme_set(theme)
EuroBioC2023
- European Bioconductor Conference 2023
- Ghent, Belgium
- September 21, 2023; 13:30 (CEST)
- See the poster also (miaverse – microbiome analytics framework in SummarizedExperiment family)!
Presenter information
All authors are affiliated to Turku Data Science Group in
University of Turku, Finland.
- Tuomas Borman – doctoral researcher
- Chouaib Benchraka – doctoral researcher
- Leo Lahti – group leader
Learning goals
- Microbiome research studies interactions between microbes (and human, environment…)
- Big data requires efficient tools to manipulate the data
- miaverse is a SummarizedExperiment framework for microbiome analytics
Figure source: Moreno-Indias et al. (2021)
Statistical and Machine Learning Techniques in Human Microbiome Studies:
Contemporary Challenges and Solutions. Frontiers in Microbiology
12:11.
Motivation
Microbiome research
- Microbiome is a composition of microbes in well-defined area (gut, skin, mouth…)
- Bilateral interaction between human and microbiome –> affects both health and disease.
- The research is based on sequencing (characterization of genes and species).
- Nowadays, multiomics approach is more common (integration of taxonomy information with metabolite data, for example)
- Computational methods are the new microscope
- The research has expanded rapidly in previous years
# Plot publication graph
path <- "data/PubMed_Timeline_Results_by_Year.csv"
df <- read.csv(path, skip = 1)
x <- "Year"
y <- "Count"
plot <- ggplot(df, aes(x = .data[[x]], y = .data[[y]])) +
geom_bar(stat="identity")
plot
PubMed publications per year with a search term ‘microbiome’ (fetched: Sep 5, 2023)
Big data
- Add here something about cohort studies, big data, how analysis is done with big data, requirements…
miaverse (MIcrobiome Analysis)
- A framework for microbiome analytics
- Based on TreeSummarizedExperiment (TreeSE) class
- Supports hierarchical data
- phyloseq class is a subset of TreeSE
- Extension of SingleCellExperiment class
- miaverse is compatible with other SummarizedExperiment frameworks
- Purpose to offer tools and tips for microbiome data analysis
- Enables development of versatile analytical workflows in microbiome
data science
- Supports multiomics (MultiAssayExperiment class)
- Scalable
- Standardized
The structure of the TreeSummarizedExperiment
(TreeSE) class.
The workflow
Importing the dataset
We get the data from MGnify database. It is a EMBL-EBI’s database for metagenomic data. This large microbiome database can be accessed with MGnifyR package which nowadays support TreeSE. The package will be submitted to Bioconductor’s next release.
We chose this dataset…
As loading takes some time, the dataset is already loaded.
For other available datasets and importing methods, check OMA.
# library(MGnifyR)
# mg <- MgnifyClient()
#
# analyses <- searchAnalysis(mg, "studies", "MGYS00005128")
# analyses <- searchAnalysis(mg, "studies", "MGYS00000596")# mae <- getResult(mg, analyses)Thanks!
- more material in OMA
- miaverse logo
- project website and QR also.
- Contact info
- Poster info
Session info
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 21
##
## Matrix products: default
## BLAS: /opt/R/4.3.1/lib/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=fi_FI.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=fi_FI.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fi_FI.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Helsinki
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] miaViz_1.9.0 ggraph_2.1.0.9000
## [3] mia_1.9.12 MultiAssayExperiment_1.27.5
## [5] TreeSummarizedExperiment_2.9.0 Biostrings_2.69.2
## [7] XVector_0.41.1 SingleCellExperiment_1.23.0
## [9] SummarizedExperiment_1.31.1 Biobase_2.61.0
## [11] GenomicRanges_1.53.1 GenomeInfoDb_1.37.3
## [13] IRanges_2.35.2 S4Vectors_0.39.1
## [15] BiocGenerics_0.47.0 MatrixGenerics_1.13.1
## [17] matrixStats_1.0.0 ggplot2_3.4.3
## [19] BiocManager_1.30.22
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.15.0 jsonlite_1.8.7
## [3] magrittr_2.0.3 ggbeeswarm_0.7.2
## [5] farver_2.1.1 rmarkdown_2.24
## [7] zlibbioc_1.47.0 vctrs_0.6.3
## [9] memoise_2.0.1 DelayedMatrixStats_1.23.4
## [11] RCurl_1.98-1.12 ggtree_3.9.1
## [13] htmltools_0.5.6 S4Arrays_1.1.5
## [15] BiocNeighbors_1.19.0 SparseArray_1.1.11
## [17] gridGraphics_0.5-1 sass_0.4.7
## [19] bslib_0.5.1 plyr_1.8.8
## [21] DECIPHER_2.29.0 cachem_1.0.8
## [23] igraph_1.5.1 lifecycle_1.0.3
## [25] pkgconfig_2.0.3 rsvd_1.0.5
## [27] Matrix_1.6-1 R6_2.5.1
## [29] fastmap_1.1.1 GenomeInfoDbData_1.2.10
## [31] digest_0.6.33 aplot_0.2.0
## [33] colorspace_2.1-0 ggnewscale_0.4.9
## [35] patchwork_1.1.3 scater_1.29.4
## [37] irlba_2.3.5.1 RSQLite_2.3.1
## [39] vegan_2.6-4 beachmat_2.17.15
## [41] labeling_0.4.3 fansi_1.0.4
## [43] polyclip_1.10-4 abind_1.4-5
## [45] mgcv_1.9-0 compiler_4.3.1
## [47] bit64_4.0.5 withr_2.5.0
## [49] BiocParallel_1.35.4 viridis_0.6.4
## [51] DBI_1.1.3 highr_0.10
## [53] ggforce_0.4.1 MASS_7.3-60
## [55] DelayedArray_0.27.10 bluster_1.11.4
## [57] permute_0.9-7 tools_4.3.1
## [59] vipor_0.4.5 beeswarm_0.4.0
## [61] ape_5.7-1 glue_1.6.2
## [63] nlme_3.1-163 grid_4.3.1
## [65] cluster_2.1.4 reshape2_1.4.4
## [67] generics_0.1.3 gtable_0.3.4
## [69] tidyr_1.3.0 BiocSingular_1.17.1
## [71] tidygraph_1.2.3 ScaledMatrix_1.9.1
## [73] utf8_1.2.3 ggrepel_0.9.3
## [75] pillar_1.9.0 stringr_1.5.0
## [77] yulab.utils_0.0.8 splines_4.3.1
## [79] dplyr_1.1.2 tweenr_2.0.2
## [81] treeio_1.25.4 lattice_0.21-8
## [83] bit_4.0.5 tidyselect_1.2.0
## [85] DirichletMultinomial_1.43.0 scuttle_1.11.2
## [87] knitr_1.43 gridExtra_2.3
## [89] xfun_0.40 graphlayouts_1.0.0
## [91] stringi_1.7.12 lazyeval_0.2.2
## [93] ggfun_0.1.2 yaml_2.3.7
## [95] evaluate_0.21 codetools_0.2-19
## [97] tibble_3.2.1 ggplotify_0.1.2
## [99] cli_3.6.1 munsell_0.5.0
## [101] jquerylib_0.1.4 Rcpp_1.0.11
## [103] parallel_4.3.1 blob_1.2.4
## [105] sparseMatrixStats_1.13.4 bitops_1.0-7
## [107] decontam_1.21.0 viridisLite_0.4.2
## [109] tidytree_0.4.5 scales_1.2.1
## [111] purrr_1.0.2 crayon_1.5.2
## [113] rlang_1.1.1